Shape-shifting robots could soon be morphing their way from the realms of science fiction into reality. Researchers have developed a metal-based material that can alter its shape and can even heal after it is damaged, conjuring up images of the T-1000 from the Terminator films. The group behind the material said it could have a number of applications, such as for the wings of an aircraft which could change their shape and even flexible skin for robots. Scientists have developed a metal-based material which can alter its shape, combining the stiffness of a metal alloy with the flexibility of a soft, porous rubber foam. The new composite material (pictured) has a stiff scaffold structure which'melts away' when needed By combining a stiff metal with soft, porous rubber foam, the team was able to combine the properties from both into a new composite material which has a stiff scaffold structure that'melts away' when needed.

Apple has developed a new tool that deconstructs iPhones after they are returned. The robotic cutting tool, called Liam, removes the valuable materials from iPhone parts so they can be repurposed into other products. Apple shared some details about the tool during a press event Monday. Some of the materials removed include cobalt, gold, silver, platinum and tungsten. For example, Tungsten is repurposed in precision cutting tools.

A new material being researched could make shape-shifting robots real, but let's hope they aren't as violent as the Transformers from the movies. Researchers have developed a flexible material that can stretch when heated and will allow robots to change shapes. The material, which can also be rigid, is a mix of elastomer foam with a soft metal alloy. With the material, robots could become more versatile. Most robots are rigid, much like human skeletons, but by morphing into new shapes, they could be used for new tasks.

For decades, futurists have predicted that commercial miners would one day tap the unimaginable mineral wealth of the world's ocean floor. Soon, that subsea gold rush could finally begin: The world's first deep-sea mining robots are poised to rip into rich deposits of copper, gold, and silver 1,600 meters down at the bottom of the Bismarck Sea, near Papua New Guinea. The massive machines, which are to be tested sometime in 2016, are part of a high-stakes gamble for the Toronto-based mining company Nautilus Minerals. Nautilus's machines have been ready to go since 2012, when a dispute between the firm and the Papua New Guinean government stalled the project. What broke the impasse was the company's offer, in 2014, to provide Papua New Guinea with certain intellectual property from the mining project.

During Chevron Corporation's (NYSE:CVX) Security Analyst meeting on March 8, several big pieces of news came out. A day before the meeting, Chevron issued a press release stating that its 54 billion Gorgon LNG facility in Australia had just started producing LNG (liquefied natural gas) and condensate. After originally estimated to be operational by the end of 2014 for under 30 billion USD, the project was delayed as costs skyrocketed. As the operator with a 47.3% stake, Chevron lost a lot of credibility due to the massive cost of its mishaps, as did its partners ExxonMobil (NYSE:XOM) and Royal Dutch Shell (NYSE:RDS.A) (NYSE:RDS.B), who each own 25% of the venture. The first cargo of LNG is expected to be shipped out very soon, potentially marking the beginning of a strong source of growth after all the headaches it took to get here.

In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors. The algorithm then applies scattering networks to the time series graphs to create the feature space. We demonstrate our method on a machine olfaction problem, where one needs to classify the gas type and the location where it originates from data sampled by an array of sensors. Our experimental results clearly demonstrate that our method outperforms classical machine learning techniques used in previous studies.

Boliden Tara Mines Ltd. consumed 184.7 GWh of electricity in 2014, equating to over 1% of the national demand of Ireland or approximately 35,000 homes. Ireland’s industrial electricity prices, at an average of 13 c/KWh in 2014, are amongst the most expensive in Europe. Cost effective electricity procurement is ever more pressing for businesses to remain competitive. In parallel, the proliferation of intelligent devices has led to the industrial Internet of Things paradigm becoming mainstream. As more and more devices become equipped with network connectivity, smart metering is fast becoming a means of giving energy users access to a rich array of consumption data. These modern sensor networks have facilitated the development of applications to process, analyse, and react to continuous data streams in real-time. Subsequently, future procurement and consumption decisions can be informed by a highly detailed evaluation of energy usage. With these considerations in mind, this paper uses variable energy prices from Ireland’s Single Electricity Market, along with smart meter sensor data, to simulate the scheduling of an industrial-sized underground pump station in Tara Mines. The objective is to reduce the overall energy costs whilst still functioning within the system’s operational constraints. An evaluation using real-world electricity prices and detailed sensor data for 2014 demonstrates significant savings of up to 10.72% over the year compared to the existing control systems.

We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.

Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the bandwidth, (iv) a method for denoising small clusters, and (v) an approach to visualizing the clusters. Combining all these enhancements gives us a complete procedure for clustering in multivariate problems. We also compare mode clustering to other clustering methods in several examples